Predictive value of progression-related gene classifier in primary non-muscle invasive bladder cancer

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Abstract

Background

While several molecular markers of bladder cancer prognosis have been identified, the limited value of current prognostic markers has created the need for new molecular indicators of bladder cancer outcomes. The aim of this study was to identify genetic signatures associated with disease prognosis in bladder cancer.

Results

We used 272 primary bladder cancer specimens for microarray analysis and real-time reverse transcriptase polymerase chain reaction (RT-PCR) analysis. Microarray gene expression analysis of randomly selected 165 primary bladder cancer specimens as an original cohort was carried out. Risk scores were applied to stratify prognosis-related gene classifiers. Prognosis-related gene classifiers were individually analyzed with tumor invasiveness (non-muscle invasive bladder cancer [NMIBC] and muscle invasive bladder cancer [MIBC]) and prognosis. We validated selected gene classifiers using RT-PCR in the original (165) and independent (107) cohorts. Ninety-seven genes related to disease progression among NMIBC patients were identified by microarray data analysis. Eight genes, a progression-related gene classifier in NMIBC, were selected for RT-PCR. The progression-related gene classifier in patients with NMIBC was closely correlated with progression in both original and independent cohorts. Furthermore, no patient with NMIBC in the good-prognosis signature group experienced cancer progression.

Conclusions

We identified progression-related gene classifier that has strong predictive value for determining disease outcome in NMIBC. This gene classifier could assist in selecting NMIBC patients who might benefit from more aggressive therapeutic intervention or surveillance.

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Most cited references 28

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Predicting recurrence and progression in individual patients with stage Ta T1 bladder cancer using EORTC risk tables: a combined analysis of 2596 patients from seven EORTC trials.

Richard J. Sylvester, Adrian P.M. van der Meijden, Willem Oosterlinck … (2006)

To provide tables that allow urologists to easily calculate a superficial bladder cancer patient's short- and long-term risks of recurrence and progression after transurethral resection. A combined analysis was carried out of individual patient data from 2596 superficial bladder cancer patients included in seven European Organization for Research and Treatment of Cancer trials. A simple scoring system was derived based on six clinical and pathological factors: number of tumors, tumor size, prior recurrence rate, T category, carcinoma in situ, and grade. The probabilities of recurrence and progression at one year ranged from 15% to 61% and from less than 1% to 17%, respectively. At five years, the probabilities of recurrence and progression ranged from 31% to 78% and from less than 1% to 45%. With these probabilities, the urologist can discuss the different options with the patient to determine the most appropriate treatment and frequency of follow-up.

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Prediction of cancer outcome with microarrays: a multiple random validation strategy

Stefan Michiels, Serge Koscielny, Catherine Hill (2005)

General studies of microarray gene-expression profiling have been undertaken to predict cancer outcome. Knowledge of this gene-expression profile or molecular signature should improve treatment of patients by allowing treatment to be tailored to the severity of the disease. We reanalysed data from the seven largest published studies that have attempted to predict prognosis of cancer patients on the basis of DNA microarray analysis. The standard strategy is to identify a molecular signature (ie, the subset of genes most differentially expressed in patients with different outcomes) in a training set of patients and to estimate the proportion of misclassifications with this signature on an independent validation set of patients. We expanded this strategy (based on unique training and validation sets) by using multiple random sets, to study the stability of the molecular signature and the proportion of misclassifications. The list of genes identified as predictors of prognosis was highly unstable; molecular signatures strongly depended on the selection of patients in the training sets. For all but one study, the proportion misclassified decreased as the number of patients in the training set increased. Because of inadequate validation, our chosen studies published overoptimistic results compared with those from our own analyses. Five of the seven studies did not classify patients better than chance. The prognostic value of published microarray results in cancer studies should be considered with caution. We advocate the use of validation by repeated random sampling.

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Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting.

Alain Dupuy, Richard M Simon (2007)

Both the validity and the reproducibility of microarray-based clinical research have been challenged. There is a need for critical review of the statistical analysis and reporting in published microarray studies that focus on cancer-related clinical outcomes. Studies published through 2004 in which microarray-based gene expression profiles were analyzed for their relation to a clinical cancer outcome were identified through a Medline search followed by hand screening of abstracts and full text articles. Studies that were eligible for our analysis addressed one or more outcomes that were either an event occurring during follow-up, such as death or relapse, or a therapeutic response. We recorded descriptive characteristics for all the selected studies. A critical review of outcome-related statistical analyses was undertaken for the articles published in 2004. Ninety studies were identified, and their descriptive characteristics are presented. Sixty-eight (76%) were published in journals of impact factor greater than 6. A detailed account of the 42 studies (47%) published in 2004 is reported. Twenty-one (50%) of them contained at least one of the following three basic flaws: 1) in outcome-related gene finding, an unstated, unclear, or inadequate control for multiple testing; 2) in class discovery, a spurious claim of correlation between clusters and clinical outcome, made after clustering samples using a selection of outcome-related differentially expressed genes; or 3) in supervised prediction, a biased estimation of the prediction accuracy through an incorrect cross-validation procedure. The most common and serious mistakes and misunderstandings recorded in published studies are described and illustrated. Based on this analysis, a proposal of guidelines for statistical analysis and reporting for clinical microarray studies, presented as a checklist of "Do's and Don'ts," is provided.

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Author and article information

Journal

Journal ID (nlm-ta): Mol Cancer

Title: Molecular Cancer

Publisher: BioMed Central

ISSN (Electronic): 1476-4598

Publication date Collection: 2010

Publication date (Electronic): 8 January 2010

Volume: 9

Page: 3

Affiliations

[1 ]Department of Urology, College of Medicine, Chungbuk National University, Cheongju, Chungbuk, South Korea

[2 ]BK21 Chungbuk Biomedical Science Center, School of Medicine, Chungbuk National University, Cheongju, Chungbuk, South Korea

[3 ]School of Electrical and Computer Engineering, Chungbuk National University, Cheongju, Chungbuk, South Korea

[4 ]Department of Food and Biotechnology, Chungju National University, Chungju, Chungbuk, South Korea

[5 ]Department of Biomedical Engineering, College of Medicine, Chungbuk National University, Cheongju, South Korea

[6 ]Department of Biochemistry, College of Medicine, Chungbuk National University, Cheongju, South Korea

Article

Publisher ID: 1476-4598-9-3

DOI: 10.1186/1476-4598-9-3

PMC ID: 2821358

PubMed ID: 20059769

SO-VID: 2921a3b2-39fc-4339-8e71-98b1d03d7c7d

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Predictive value of progression-related gene classifier in primary non-muscle invasive bladder cancer

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Abstract

Background

Results

Conclusions

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Karger: Oncology

Most cited references 28

Predicting recurrence and progression in individual patients with stage Ta T1 bladder cancer using EORTC risk tables: a combined analysis of 2596 patients from seven EORTC trials.

Prediction of cancer outcome with microarrays: a multiple random validation strategy

Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting.

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